CALCULATION AND VISUALIZATION OF WATER CONSUMPTION RATES OF CROPS WHEN USING INFORMATION TECHNOLOGIES
Abstract
The article analyzes existing approaches to determining water consumption rates of crops for irrigation in Ukraine. They were estimated at the level of weather stations and regions, in view of climate change and the need for their constant updating using the developed automated system and information technologies. It was found that water need for growing crops has increased significantly, especially in the southern regions of Ukraine. This work is a continuation of the study of evapotranspiration, its components and dynamics based on remote sensing data and calculations when using the Penman-Monteith-Leuning method. The obtained results are presented in an interactive database and as visualized cartographic information. The rate calculation was carried out based on the potential evapotranspiration for the period 2005–2024, when using the biophysical Shtoiko method, which allows determining water consumption rates taking into account a natural moisture deficit. Meteorological data from regional weather stations operating in automatic mod as well as the information systems developed at the Institute of Water Problems and Land Reclamation were used for calculations. Water consumption was estimated based on water balance equations and multi-year series of agricultural and meteorological observations.
The average annual sowing dates and development phases of the main crops in the regions of Ukraine were also specified, with reference to weather stations, and the maps of water consumption spatial distribution were built. A database of crop water consumption rates was created with integrating geospatial parameters. Python software was developed using the Folium, Shapely, and Django libraries for data analysis and visualization. For geospatial presentation of the results, the zones of weather stations influence were calculated using the Thyssen-Voronyi polygon method. The study revealed a significant increase in water consumption rates for crops in Ukraine over the past two decades compared to the control climatic period of 1960-2000. In the Steppe zone, water consumption increased by 40%, in the Forest-Steppe and Polissya zones - by 15%. Data analysis for 2005-2024.
Analysis of data for 2005-2024 confirmed a further increase in water consumption in all climatic zones by an average of 18-25%. Combining these data with web tools increases the availability of information and promotes its practical use in agriculture. Maps of water consumption deficits for the warm period of the year, water consumption rates for corn and wheat for the years of 50%, 75% and 95% water supply deficit, which reflect regional variability in their distribution, were built. The study confirmed the need for constant updating of water consumption rates and their consideration in planning agricultural policy and water management.
References
2. Tymchasovi raionovani normy vodopotreb sіlskohospodarskykh kultur dlia zroshennia doshchuvanniam [Temporary zonal water demand norms for irrigated crops using sprinklers] (2015). Agrarna Nauka. [in Ukrainian].
3. Metodychni rekomendatsii z operatyvnoho planuvannia rezhymiv zroshennia [Methodical recommendations for operational planning of irrigation regimes] (2004). IGiM UAAN, IZPR UAAN. [in Ukrainian].
4. Open Data Portal. (n.d.). Register of issued permits for special water use. Retrieved: October 20, 2025, from: https://data.gov.ua/dataset/water-use-permits
5. State Environmental Inspection of Ukraine. (n.d.). Retrieved from: https://www.dei.gov.ua/post/2225
6. Romashchenko, M. I., Husyev, Y. V., Shatkovskyi, A. P., Saydak, R. V., Yatsyuk, M. V., Shevchenko, A. M. & Matyash, T. V. (2020). Impact of climate change on water resources and agricultural production. Melioratsiya i vodne hospodarstvo, (1), 5-22. https://doi.org/10.31073/mivg202001-235
7. Haile, G. G., Tang, Q., Reda, K. W., Baniya, B., He, L., Wang, Y., & Gebrechorkos, S. H. (2024). Projected impacts of climate change on global irrigation water withdrawals. Agricultural Water Management, 305, 109144. https://doi.org/10.1016/j.agwat.2024.109144
8. El-Fakharany, Z. M., & Salem, M. G. (2021). Mitigating climate change impacts on irrigation water shortage using brackish groundwater and solar energy. Energy Reports, 7, 608-621. DOI:10.1016/j.egyr.2021.07.091
9. Rosa, L., Ragettli, S., Sinha, R., Zhovtonog, O., Yu, W., & Karimi, P. (2024). Regional irrigation expansion can support climate-resilient crop production in post-invasion Ukraine. Nature Food, 5(8), 684-692. DOI:10.1038/s43016-024-01017-7
10. Casa, R., Rossi, M., Sappa, G., & Trotta, A. (2009). Assessing crop water demand by remote sensing and GIS for the Pontina Plain, Central Italy. Water Resources Management, 23, 1685-1712. DOI:10.1007/s11269-008-9347-4
11. Parmar, S. H., Patel, G. R., & Tiwari, M. K. (2023). Assessment of crop water requirement of maize using remote sensing and GIS. Smart Agricultural Technology, 4, 100186. https://doi.org/10.1016/j.atech.2023.100186
12. Matiash, T. V., Butenko, Y. O., Smirnov, A. M., & Matiash, E. I. (2024). Assessment of the evapotranspiration components dynamics in different agro-climatic zones of Ukraine using the Penman-Monteith-Leuning model. Land Reclamation and Water Management, (2), 34-44. https://doi.org/10.31073/mivg202402-398
13. Beeson, R. C. Jr. (2011). Weighing lysimeter systems for quantifying water use and studies of controlled water stress for crops grown in low bulk density substrates. Agricultural Water Management, 98(6), 967-976. https://doi.org/10.1016/j.agwat.2011.01.005
14. Denager, T., et al. (2020). Comparison of evapotranspiration estimates using the water balance and the eddy covariance methods. Vadose Zone Journal, 19(1). https://doi.org/10.1002/vzj2.20032
15. Ragab, R., Evans, J. G., Battilani, A., & Solimando, D. (2017). Towards accurate estimation of crop water requirement without the crop coefficient Kc: New approach using modern technologies. Irrigation and Drainage, 66, 469-477. https://doi.org/10.1002/ird.2153
16. Ragab, R., Evans, J. G., Battilani, A., & Solimando, D. (2017). The Cosmic-ray Soil Moisture Observation System (Cosmos) for estimating the crop water requirement: New approach. Irrigation and Drainage, 66, 456-468. https://doi.org/10.1002/ird.2152
17. Roja, M. (2020). Estimation of crop water requirement of maize crop using FAO CROPWAT 8.0 Model. Indian Journal of Pure & Applied Biosciences, 8(6), 222-228. https://doi.org/10.18782/2582-2845.8148
18. Roja, M. (2020). Estimation of crop water requirement of maize crop using FAO CROPWAT 8.0 Model. Indian Journal of Pure & Applied Biosciences, 8(6), 222-228. https://doi.org/10.18782/2582-2845.8148
19. Food & Agriculture Organization of the United Nations (FAO). (1992). Cropwat (126 p.).
20. FAO. (2021). The AquaCrop model – Enhancing crop water productivity. https://doi.org/10.4060/cb7392en
21. Adnan, M., et al. (2017). Estimating Evapotranspiration using Machine Learning Techniques. International Journal of Advanced Computer Science and Applications, 8(9). https://doi.org/10.14569/ijacsa.2017.080915
22. Baliuk, S. A., Romashchenko, M. I., & Stashuk, V. A. (2009). Naukovi osnovy okhorony ta ratsionalnoho vykorystannia zroshuvanykh zemel Ukrainy [Scientific foundations of the protection and rational use of irrigated lands in Ukraine]. Agrarna Nauka. [in Ukrainian].
23. Kovalenko, P. I. (Ed.). (2016). Intehrovane upravlinnia vodnymy i zemelnymy resursamy na meliorovanykh terytoriiakh [Integrated management of water and land resources in reclaimed areas]. Agrarna Nauka. [in Ukrainian].
24. Metodychni rekomendatsii z operatyvnoho planuvannia rezhymiv zroshennia [Methodical recommendations for operational planning of irrigation regimes] (2004). IGiM UAAN, IZPR UAAN. [in Ukrainian].
25. Shtoiko, D. A., & Pysarenko, V. A. (1967). Rekomendatsii po rezhimu zroshennia silskohospodarskykh kultur [Recommendations on the irrigation regime of agricultural crops]. Urozhai. [in Ukrainian].
26. Rozrakhunok norm vodopotreby [Calculation of water demand norms]. (n.d.). Retrieved from http://185.168.130.174:90/m/
27. iwpim. (n.d.). Retrieved from http://185.168.130.174:90/
28. Klimat Ukrainy [Climat of Ukraine] (2003). Scientific edition. ed. V.M. Lipinsky, V.A. Dyachuk, V.M. Babichenko/Ukr. nauk.-dosl. hydrometeorological. inst. – K.: Publishing of Raevsky.[in Ukrainian]
29. Matiash, T. V., et al. (2024). Instytut vodnykh problem i melioratsii NAAN. Zvit pro naukovo-doslidnu robotu “Rozroblennia teoretychnykh osnov system pidtrymky pryiniattia rishen u zroshenni na osnovi poiednannia danykh riznoi pryrody” (Zavdannia 04.02.00.12.F). “Doslidzhennia zakonomirnostei zmin sezonnoi vodopotreby kultur v umovakh zmin klimatu” [Institute of Water Problems and Land Reclamation. Report on the research project “Development of theoretical foundations of decision support systems in irrigation based on the integration of heterogeneous data”]. [in Ukrainian].
30. Kovalchuk, P. Ι., Matiash, T. V., Kovalchuk, V. P., Demchuk, O. S., Balykhina, H. A., Gerus, A. V., & Pendak, N. V. (2019). Systemne modeliuvannia i upravlinnia vodo-i zemlekorystuvanniam [System modeling and management of water and land use]. Kyiv: Ahrarna nauka. [in Ukrainian].
31. Mezentsev, V. S. (Ed.). (1974). Rezhimy vlagoobespechennosti i usloviia gidromelioratsii stepnogo kraia [Moisture regimes and hydromelioration conditions of the steppe region]. Moscow: Kolos. [in Russian].
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., ... & van Mulbregt, P. (2020). SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17, 261–272. https://doi.org/10.1038/s41592-019-0686-2
